README.md

metadata

license: cc-by-nc-4.0

Baidu ULTR Dataset - Baidu BERT-12l-12h

Setup

1. Install huggingface datasets
2. Install pandas and pyarrow: pip install pandas pyarrow
3. Optionally, you might need to install a pyarrow-hotfix if you cannot install pyarrow >= 14.0.1
4. You can now use the dataset as described below.

Load train / test click dataset:

from datasets import load_dataset

dataset = load_dataset(
    "philipphager/baidu-ultr_baidu-mlm-ctr",
    name="clicks",
    split="train", # ["train", "test"]
    cache_dir="~/.cache/huggingface",
)

dataset.set_format("torch") #  [None, "numpy", "torch", "tensorflow", "pandas", "arrow"]

Load expert annotations:

from datasets import load_dataset

dataset = load_dataset(
    "philipphager/baidu-ultr_baidu-mlm-ctr",
    name="annotations",
    split="test",
    cache_dir="~/.cache/huggingface",
)

dataset.set_format("torch") #  [None, "numpy", "torch", "tensorflow", "pandas", "arrow"]

Example PyTorch collate function

Each sample in the dataset is a single query with multiple documents. The following example demonstrates how to create a batch containing multiple queries with varying numbers of documents by applying padding:

import torch
from typing import List
from collections import defaultdict
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader


def collate_clicks(samples: List):
    batch = defaultdict(lambda: [])

    for sample in samples:
        batch["query_document_embedding"].append(sample["query_document_embedding"])
        batch["position"].append(sample["position"])
        batch["click"].append(sample["click"])
        batch["n"].append(sample["n"])

    return {
        "query_document_embedding": pad_sequence(batch["query_document_embedding"], batch_first=True),
        "position": pad_sequence(batch["position"], batch_first=True),
        "click": pad_sequence(batch["click"], batch_first=True),
        "n": torch.tensor(batch["n"]),
    }

loader = DataLoader(dataset, collate_fn=collate_clicks, batch_size=16)